[1] P. Angelov and X. Zhou, On line learning fuzzy rule-based system structure from data
streams, In 2008 IEEE International Conference on Fuzzy Systems within the IEEE World
Congress on Computational Intelligence, Hong Kong., (2008), 915-922.
[2] J. F. Baldwin and S. B. Kandarake, Asymmetric triangular fuzzy sets for classi cation model,
In Lecture Notesin Arti cial Intelligent, (2003), 364-370.
[3] J. N. Choi, S. K. Oh and W. Pedrycz., Identi cation of fuzzy models using a successive tuning
method with a variant identi cation ratio, Fuzzy Sets and Systems, 159 (2008), 2873-2889.
[4] B. L. R. De Moor, ed., DaISy: Database for the Identi cation of Systems, De-
partment of Electrical Engineering, 2008, ESAT/SISTA, K.U.Leuven, Belgium, URL:
http://homes.esat.kuleuven.be/~smc/daisy/. (visited on Oct. 10, 2010).
[5] H. Du and N. Zhang, Application of evolving Takagi-Sugeno fuzzy model to nonlinear system
identi cation, Applied soft computing, 8 (2007), 676-686.
[6] A. Fiordaliso, A constrained Takagi-Sugeno fuzzy system that allows for better interpretation
and analysis, Fuzzy Sets and Systems, 118 (2001), 307-318.
[7] M. Hell, S. P. Campinas, R. Ballini, Jr. P. Costa and F. Gomid, Training neurofuzzy networks
with participatory learning, In: proceeding of 5th Conference of the EUSFLAT, (2007), 231-
236, .
[8] J. S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst.
Man and Cybern, 23 (1993), 665-685.
[9] A. Kalhor, B. N. Araabi and C. Lucas, A new systematic design for habitually linear evolving
TS fuzzy model, Journal of Expert systems with applications, 39 (2012), 1725-1736.
[10] A. Kalhor, B. N. Araabi and C. Lucas, An online predictor model as adaptive habitually
linear and transiently nonlinear model, Evolving Systems, 1 (2010), 29-41.
[11] A. Kalhor, B. N. Araabi and C. Lucas, A new high-order Takagi-Sugeno fuzzy model based on
deformed linear models, Amirkabir Int. J. of Modeling Identi cation, Simulation and Control,
42 (2010), 43-52.
[12] A. Kalhor, B. N. Araabi and C. Lucas, Reducing the number of local linear models in neuro{
fuzzy modeling: A split-and-merge clustering approach, Applied Soft Computing Journal, 11
(2011), 5582{5589.
[13] N. Kasabov, DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its application
for time-series prediction, IEEE Trans. Fuzzy Syst., 10 (2012) 144-154.
[14] V. Krkov: Kolmogorov's theorem and multilayer neural networks, Neural networks 5: 501-
506, 1992.
[15] D. H. Lee, Y. H. Joo and M. H. Tak, Local stability analysis of continuous-time Takagi{
Sugeno fuzzy systems: A fuzzy Lyapunov function approach, Information Sciences, 257
(2014), 163-175.
[16] C. H. Lee and H. Y. Pan, Performance enhancement for neural fuzzy systems using asym-
metric membership functions, Fuzzy Sets and Systems, 160 (2009), 949-971.
[17] C. H. Lee and C. C. Teng, Fine tuning of membership functions for fuzzy neural systems,
Asian J. Control, 3 (2001), 216-225.
[18] G. Leng, T. M. Mc Ginnity and G. Prasad, An approach for on-line extraction of fuzzy rules
using a self-organising fuzzy neural network, Fuzzy Sets and Systems, 150 (2005), 211-243.
[19] C. Li C, K. H. Cheng and J. D. Lee, Hybrid learning neuro fuzzy approach for complex
modeling using asymmetric fuzzy sets, In: Proc. of the17th IEEE International Conf. on
Tools with Arti cial Intelligence, (2005), 397-401.
[20] C. Li, J. Zhou, X. Xiang, Q. Li and X. An, T-S fuzzy model identi cation based on a novel
fuzzy c-regression model clustering algorithm, Engineering Applications of Arti cial Intelligence,
22 (2009), 646-653.
[21] C. J. Lin and W. H. Ho, An asymmetric-similarity-measure-based neural fuzzy inference
system, Fuzzy Sets and Systems, 152 (2005), 535{551.
[22] T. J. Mc Avoy, E. Hsu and S. Lowenthal, Dynamics of pH in controlled stirred tank reactor,
Ind. Eng. Chem. Process Des. Develop., 11 (1972), 71-78.
[23] O. Nelles, Nonlinear System Identi cation, In: New York: Springer, (2001), Section 13.3.1,
365-37.
[24] P. Nikdel, M. Hosseinpour, M. A. Badamchizadeh and M. A. Akbari, Improved Takagi{
Sugeno fuzzy model-based control of
exible joint robot via Hybrid-Taguchi genetic algorithm,
Engineering Applications of Arti cial Intelligence, 33 (2014), 12-20.
[25] J. Park and I. W. Sandberg, Universal Approximation Using Radial-Basis-Function Net-
works, Neural Computation, 3 (1991), 246-257.
[26] K. B. Petersen and M. S. Pedersen, The matrix cookbook, http://matrixcookbook.com, Version:
Nov. 14, 2008.
[27] Y. Shi, R. Eberhart and Y. Chen, Implementation of evolutionary fuzzy systems, IEEE Trans.
on Fuzzy Sys., 7 (1999), 109-118.
[28] T. Takagi and M. Sugeno, Fuzzy identi cation of systems and its applications to modeling
and control, IEEE Trans. Syst., Man, and Cybern., 15 (1985), 116-132.
[29] D. Wang, C. Quek and G. S. Ng, MS-TSKfnn: Novel Takagi-Sugeno-Kang fuzzy neural
network using ART like clustering., In: proceeding of IEEE international joint conference on
Neural Networks., (2004), 2361-2366.
[30] X. Xie, L. Lin and S. Zhong, Process Takagi{Sugeno model: A novel approach for han-
dling continuous input and output functions and its application to time series prediction,
Knowledge-Based Systems, 63 (2014), 46-58.
[31] H. Ying, General SISO Takagi{Sugeno fuzzy systems with linear rule consequent are univer-
sal approximators, IEEE Trans. on Fuzzy Systems, 6 (1998), 582-587.
)